code: def get_parser():
      parser = config_argparse.ArgumentParser(
          description="UASR Decoding",
          formatter_class=argparse.ArgumentDefaultsHelpFormatter,
      )
  
      # Note(kamo): Use '_' instead of '-' as separator.
      # '-' is confusing if written in yaml.
      parser.add_argument(
          "--log_level",
          type=lambda x: x.upper(),
          default="INFO",
          choices=(
              "CRITICAL",
              "ERROR",
              "WARNING",
              "INFO",
              "DEBUG",
              "NOTSET",
          ),
          help="The verbose level of logging",
      )
  
      parser.add_argument("--output_dir", type=str, required=True)
      parser.add_argument(
          "--ngpu",
          type=int,
          default=0,
          help="The number of gpus. 0 indicates CPU mode",
      )
      parser.add_argument(
          "--seed", type=int, default=0, help="Random seed"
      )
      parser.add_argument(
          "--dtype",
          default="float32",
          choices=["float16", "float32", "float64"],
          help="Data type",
      )
      parser.add_argument(
          "--num_workers",
          type=int,
          default=1,
          help="The number of workers used for DataLoader",
      )
  
      group = parser.add_argument_group("Input data related")
      group.add_argument(
          "--data_path_and_name_and_type",
          type=str2triple_str,
          required=True,
          action="append",
      )
      group.add_argument("--key_file", type=str_or_none)
      group.add_argument(
          "--allow_variable_data_keys", type=str2bool, default=False
      )
  
      group = parser.add_argument_group(
          "The model configuration related"
      )
      group.add_argument(
          "--uasr_train_config",
          type=str,
          help="UASR training configuration",
      )
      group.add_argument(
          "--uasr_model_file",
          type=str,
          help="UASR model parameter file",
      )
      group.add_argument(
          "--lm_train_config",
          type=str,
          help="LM training configuration",
      )
      group.add_argument(
          "--lm_file",
          type=str,
          help="LM parameter file",
      )
      group.add_argument(
          "--word_lm_train_config",
          type=str,
          help="Word LM training configuration",
      )
      group.add_argument(
          "--word_lm_file",
          type=str,
          help="Word LM parameter file",
      )
      group.add_argument(
          "--model_tag",
          type=str,
          help="Pretrained model tag. If specify this option, *_train_config and "
          "*_file will be overwritten",
      )
  
      group = parser.add_argument_group("Beam-search related")
      group.add_argument(
          "--batch_size",
          type=int,
          default=1,
          help="The batch size for inference",
      )
      group.add_argument(
          "--nbest",
          type=int,
          default=1,
          help="Output N-best hypotheses",
      )
      group.add_argument(
          "--beam_size", type=int, default=20, help="Beam size"
      )
      group.add_argument(
          "--penalty", type=float, default=0.0, help="Insertion penalty"
      )
      group.add_argument(
          "--maxlenratio",
          type=float,
          default=0.0,
          help="Input length ratio to obtain max output length. "
          "If maxlenratio=0.0 (default), it uses a end-detect "
          "function "
          "to automatically find maximum hypothesis lengths",
      )
      group.add_argument(
          "--minlenratio",
          type=float,
          default=0.0,
          help="Input length ratio to obtain min output length",
      )
      group.add_argument(
          "--ctc_weight",
          type=float,
          default=0.5,
          help="CTC weight in joint decoding",
      )
      group.add_argument(
          "--lm_weight", type=float, default=1.0, help="RNNLM weight"
      )
      group.add_argument("--streaming", type=str2bool, default=False)
  
      group = parser.add_argument_group("Text converter related")
      group.add_argument(
          "--token_type",
          type=str_or_none,
          default=None,
          choices=["phn", "word"],
          help="The token type for UASR model. "
          "If not given, refers from the training args",
      )
      group.add_argument(
          "--bpemodel",
          type=str_or_none,
          default=None,
          help="The model path of sentencepiece. "
          "If not given, refers from the training args",
      )
      group.add_argument(
          "--is_ctc_decoding",
          type=str2bool,
          default=True,
          help="Use ctc topology as decoding graph",
      )
      group.add_argument(
          "--use_nbest_rescoring", type=str2bool, default=False
      )
      group.add_argument(
          "--num_paths",
          type=int,
          default=1000,
          help="The third argument for k2.random_paths",
      )
      group.add_argument(
          "--nbest_batch_size",
          type=int,
          default=500,
          help="batchify nbest list when computing am/lm scores to avoid OOM",
      )
      group.add_argument(
          "--nll_batch_size",
          type=int,
          default=100,
          help="batch_size when computing nll during nbest rescoring",
      )
      group.add_argument(
          "--decoding_graph", type=str, help="decoding graph"
      )
      group.add_argument(
          "--word_token_list",
          type=str_or_none,
          default=None,
          help="output token list",
      )
      group.add_argument(
          "--k2_config",
          type=str,
          help="Config file for decoding with k2",
      )
  
      return parser
  
imports: import argparse
  import datetime
  import logging
  import sys
  from pathlib import Path
  from typing import Any, List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  import yaml
  from typeguard import check_argument_types, check_return_type
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.lm import LMTask
  from espnet2.tasks.uasr import UASRTask
  from espnet2.text.build_tokenizer import build_tokenizer
  from espnet2.text.token_id_converter import TokenIDConverter
  from espnet2.torch_utils.device_funcs import to_device
  from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
  from espnet2.utils import config_argparse
  from espnet2.utils.types import str2bool, str2triple_str, str_or_none
  from espnet.nets.pytorch_backend.transformer.subsampling import TooShortUttError
  from espnet.utils.cli_utils import get_commandline_args
label_desc: Use image node_id 3 for calling a global function with example usage: get_parser() and returns: parser